Apparatus and method for performing color conversions using machine learning

ABSTRACT

An information processing apparatus storing a machine-learned model that learned, by machine learning, a relationship between a type of a printing medium, an amount of a coloring material on the printing medium per unit area, and an image printed on the printing medium; and estimating, based on a selection information and a imaging information, by using the machine-learned model a limit value indicating a maximum value or a minimum value of an amount of the coloring material to be used in printing on the printing medium by the printing section per unit area; and creating, by using the limit value, a color conversion profile including information regarding mapping between a coordinate value in a color space and an amount of the coloring material.

The present application is based on, and claims priority from JPApplication Serial Number 2019-133758, filed Jul. 19, 2019, thedisclosure of which is hereby incorporated by reference herein in itsentirety.

BACKGROUND 1. Technical Field

The present disclosure relates to an information processing apparatus, acolor conversion profile creation method, and a learning apparatus.

2. Related Art

A printing apparatus that prints an image on a printing medium such aspaper or film by using a coloring material such as ink or toner isknown. This type of printing apparatus generally prints coloringmaterials of a plurality of colors separately on a printing medium tothereby print an image on the printing medium. In this regard, a colorconversion profile is used for converting a color value of imageinformation into an amount of a coloring material of each color. In thecreation of the color conversion profile, to improve printing quality,it is necessary to appropriately set a limit value such as an upperlimit value of an amount of a coloring material to be used for printingon a printing medium per unit area.

For example, an apparatus described in JP-A-2018-126993 determines anupper limit value of a discharge amount of ink so as to avoid a certaineffect. The apparatus determines the upper limit value based on imaginginformation obtained by capturing an image of a printing medium on whicha test pattern is printed and color measurement information obtained bymeasuring the color of the printing medium. Here, an upper limit valueof the discharge amount at which overflowing, bleeding, or aggregationof the ink does not occur is determined based on the imaginginformation. Further, an upper limit value of the discharge amount atwhich color saturation of the ink does not occur is determined based onthe color measurement information. A threshold value determined inadvance in sensory evaluation performed by a plurality of people is usedfor each determination described above.

However, the apparatus described in JP-A-2018-126993 needs to performprinting and imaging for each effect to be avoided. Therefore, in theapparatus described in JP-A-2018-126993, many man-hours are required forcreating the color conversion profile.

SUMMARY

According to an aspect of the present disclosure, an informationprocessing apparatus includes: a storage section configured to store amachine-learned model that learned, by machine learning, a relationshipbetween a type of a printing medium, an amount of a coloring material onthe printing medium per unit area, and an image printed on the printingmedium; a receiving section configured to receive input of selectioninformation including medium-type information regarding a type of theprinting medium; an obtaining section configured to obtain imaginginformation obtained by capturing the image printed on the printingmedium by a printing section that performs printing by using thecoloring material; an estimating section configured to estimate, basedon the selection information and the imaging information by using themachine-learned model, a limit value indicating a maximum value or aminimum value of an amount of the coloring material to be used forprinting on the printing medium by the printing section per unit area;and a creating section configured to create, by using the limit value, acolor conversion profile including information regarding mapping betweena coordinate value in a color space and an amount of the coloringmaterial.

According to an aspect of the present disclosure, a color conversionprofile creation method includes: preparing a machine-learned model thatlearned, by machine learning, a relationship between a type of aprinting medium, an amount of a coloring material on the printing mediumper unit area, and an image printed on the printing medium; receivinginput of selection information including medium-type informationregarding a type of the printing medium and obtaining imaginginformation obtained by capturing the image printed on the printingmedium by a printing section that performs printing by using thecoloring material; estimating, based on the selection information andthe imaging information by using the machine-learned model, a limitvalue indicating a maximum value or a minimum value of an amount of thecoloring material to be used for printing on the printing medium by theprinting section per unit area; and creating, by using the limit value,a color conversion profile including information regarding mappingbetween a coordinate value in a color space and an amount of thecoloring material.

According to an aspect of the present disclosure, a non-transitorycomputer-readable storage medium storing a color conversion profilecreation program causing a computer to implement: a receiving functionof receiving input of selection information including medium-typeinformation regarding a type of a printing medium; an obtaining functionof obtaining imaging information obtained by capturing an image printedon the printing medium by a printing section performing printing byusing a coloring material; an estimating function of estimating, basedon the selection information and the imaging information, a limit valueindicating a maximum value or a minimum value of an amount of thecoloring material to be used for printing on the printing medium by theprinting section per unit area, by using a machine-learned model thatlearned, by machine learning, a relationship between a type of aprinting medium, an amount of the coloring material on the printingmedium per unit area, and the image printed on the printing medium; anda creating function of creating, by using the limit value, a colorconversion profile including information regarding mapping between acoordinate value in a color space and an amount of the coloringmaterial.

According to an aspect of the present disclosure, a learning apparatusincludes: an input section configured to receive an input of a data setin which are mapped selection information including medium-typeinformation regarding a type of a printing medium, imaging informationobtained by capturing an image printed on the printing medium by aprinting section that performs printing by using a coloring material,and a limit value indicating a maximum value or a minimum value of anamount of the coloring material to be used for printing on the printingmedium by the printing section per unit area; and a learning processingsection configured to generate, based on the data set, a machine-learnedmodel that learned, by machine learning, a relationship between a typeof the printing medium, an amount of the coloring material on theprinting medium per unit area, and the image printed on the printingmedium.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an example of a configurationof a system using an information processing apparatus according to anembodiment.

FIG. 2 is a diagram illustrating an example of a color conversion tableincluded in a color conversion profile.

FIG. 3 is a diagram illustrating a flow of creating the color conversionprofile.

FIG. 4 is a diagram illustrating an example of an image used in creatingthe color conversion profile.

FIG. 5 is a diagram illustrating an example of a unit image constitutingthe image illustrated in FIG. 4.

FIG. 6 is a diagram illustrating an example of a state where overflowingof ink occurs.

FIG. 7 is a diagram illustrating an example of a state where bleeding ofink occurs.

FIG. 8 is a diagram illustrating an example of a state where aggregationof ink occurs.

FIG. 9 is a diagram for describing an estimating section estimating alimit value of an amount of a coloring material.

FIG. 10 is a diagram for describing machine learning for generating amachine-learned model.

FIG. 11 is a schematic diagram illustrating an example of aconfiguration of a system using an information processing apparatusaccording to a modified example.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, exemplary embodiments according to the present disclosurewill be described with reference to the accompanying drawings. Note thatthe dimensions or the scale of each component may differ appropriatelyfrom actual dimensions or scales, and some portions are schematicallyillustrated in the drawings to facilitate understanding. Further, thescope of the present disclosure is not limited to the embodiments unlessotherwise specified in the following description.

1. Overview of System 100 Using Information Processing Apparatus 1

FIG. 1 is a schematic diagram illustrating an example of a configurationof a system 100 that uses an information processing apparatus 1according to an embodiment. The system 100 has a function of performingprinting by using an ink jet-type printing apparatus 200 that uses inkas a coloring material, and a function of creating a color conversionprofile DP used for color matching in the printing performed by theprinting apparatus 200. The printing apparatus 200 is an example of a“printing section”. The system 100 includes the printing apparatus 200,an imaging apparatus 300, a learning apparatus 400, and the informationprocessing apparatus 1. Each of the printing apparatus 200, the imagingapparatus 300, and the learning apparatus 400 is communicably connectedto the information processing apparatus 1 in a wired or wireless manner.Note that the connection may be established via a communication networksuch as the Internet. Further, the learning apparatus 400 does not haveto be communicably connected to the information processing apparatus 1as long as the learning apparatus 400 can transmit and receive necessaryinformation to and from the information processing apparatus 1.

The printing apparatus 200 is an ink jet-type printer that performsprinting on a printing medium according to control by the informationprocessing apparatus 1. It is sufficient that the printing medium be amedium on which the printing apparatus 200 can perform printing. Theprinting medium is not particularly limited, and examples of theprinting medium include various types of paper, fabrics, and films. Theprinting apparatus 200 illustrated in FIG. 1 includes an ink ejectinghead 210 ejecting ink of four colors: cyan, magenta, yellow, and black.Further, although not illustrated, the printing apparatus 200 includes atransport mechanism for transporting the printing medium in apredetermined direction, and a moving mechanism that moves the inkejecting head 210 back and forth along an axis orthogonal to a transportdirection of the printing medium.

The ink ejecting head 210 includes a C ejecting portion 211C forejecting cyan ink, a M ejecting portion 211M for ejecting magenta ink, aY ejecting portion 211Y for ejecting yellow ink, and a K ejectingportion 211K for ejecting black ink. These ejecting portions each eject,onto the printing medium, ink supplied from an ink container (notillustrated) through a plurality of nozzles (not illustrated). Morespecifically, each ejecting portion includes a pressure chamber and adriving element (not illustrated) for a corresponding nozzle, and aspressure in the pressure chamber is changed by the driving element, inkin the pressure chamber is ejected from the nozzle. Examples of thedriving element include a piezoelectric element and a heating element.In the above-described printing apparatus 200, since the reciprocatingmovement of the ink ejecting head 210 and the ejection of the ink areperformed in parallel, an image is formed on a printing surface of theprinting medium transported.

Note that the moving mechanism for moving the ink ejecting head 210backwards and forwards may be omitted. In this case, for example, theink ejecting head 210 may be provided over an entire region in a widthdirection orthogonal to the transport direction of the printing medium.Further, the number of colors of ink ejected from the ink ejecting head210 is not limited to four and may be three or less, or five or more.

The imaging apparatus 300 is an apparatus, such as a camera or ascanner, for capturing an image of a printing surface of a printingmedium after printing performed by the printing apparatus 200. Imaginginformation indicating a captured image of the printing surface isgenerated by the imaging. When an image based on test image informationDG is printed on the printing surface, the imaging information isimaging information DI. The imaging apparatus 300 includes, for example,an imaging optical system and an imaging element. The imaging opticalsystem is an optical system including at least one image capturing lens,and may include various types of optical elements such as a prism, ormay include a zoom lens, a focusing lens, or the like. Examples of theimaging element include a charge-coupled device (CCD) image sensor and acomplementary metal oxide semiconductor (CMOS) image sensor.

Note that the imaging apparatus 300 may have a spectroscopic function.In this case, for example, a diffraction grid or a wavelength-tunablefilter may be provided in the imaging optical system. The image capturedby the imaging apparatus 300 may be a full-color image or a monochromeimage. When the captured image is a full-color image, the imaginginformation DI is represented by, for example, tristimulus values in anXYZ color system for each pixel of the captured image. When the capturedimage is a monochrome image, the imaging information DI is representedby, for example, a brightness value for each pixel of the capturedimage. However, the captured image is preferably a full-color image.

The information processing apparatus 1 is a computer that controlsoperations of the printing apparatus 200 and the imaging apparatus 300.The information processing apparatus 1 has a profile creation functionof creating a color conversion profile DP by using a machine-learnedmodel PJ provided from the learning apparatus 400, and a printingexecution function of executing printing performed by the printingapparatus 200 by using the color conversion profile DP. In the presentembodiment, the information processing apparatus 1 has, in addition tothe above-described functions, a function of performing additionalmachine learning of the machine-learned model PJ based on a result ofevaluation by a user. These functions are implemented by executing acolor conversion profile creation program P1.

The color conversion profile DP includes a color conversion table TBLand a limit value D4. The color conversion table TBL includesinformation regarding mapping between a coordinate value in a colorspace, and an amount of ink. The color space is, for example, adevice-dependent color space such as an RGB color space or a CMYK colorspace. The amount of ink is a supplied amount of ink of each of aplurality of colors used by the printing apparatus 200 per unit area ofthe printing surface of the printing medium. The limit value D4 is anupper limit value of the amount of ink of a single color, a secondarycolor, or a higher-order color in the color conversion table TBL. Thatis, the limit value D4 represents a maximum value of the amount of inkto be used for printing on the printing medium by the printing apparatus200 per unit area. The limit value D4 is estimated based on selectioninformation D0 by using the machine-learned model PJ so that printingquality satisfies a predetermined condition. Note that the format of thecolor conversion profile DP is not particularly limited, and, forexample, may conform to International Color Consortium (ICC) standards.

The machine-learned model PJ is an estimation model that learned, bymachine learning, a relationship between a type of the printing medium,an amount of the coloring material on the printing medium per unit area,and an image printed on the printing medium. The selection informationD0 includes at least medium-type information D1 among the medium-typeinformation D1, color space information D2, and coloring material-typeinformation D3. The medium-type information D1 is information regardingthe type of the printing medium. Examples of the type of the printingmedium can include plain paper, vinyl chloride sheets, and tarpaulins.The color space information D2 is information regarding the type of thecolor space that is a color matching standard. Examples of the colormatching standard can include Japan Color, Specification for Web OffsetPublication (SWOP), Euro Standard, and other standard color values. Thecoloring material-type information D3 is information regarding the typeof the coloring material used by the printing apparatus 200. Examples ofthe type of the coloring material can include a serial number of ink oran ink set. In other words, the coloring material-type information D3 isinformation regarding the type of the printing apparatus 200. Note that“Japan Color” is a registered trademark. Further, the medium-typeinformation D1 may be information regarding a model number of theprinting medium.

The learning apparatus 400 is a computer that generates themachine-learned model PJ. The learning apparatus 400 generates themachine-learned model PJ through supervised machine learning using adata set DS as training data. Although not illustrated in FIG. 1, thedata set DS includes the selection information D0, the imaginginformation DI, and a limit value D4 a. The limit value D4 a is a labelcorresponding to a correct value. The generation of the machine-learnedmodel PJ in the learning apparatus 400 may simply be performed atappropriate times and may be performed based on a user instruction ormay be automatically performed on a regular basis. The machine-learnedmodel PJ generated by the learning apparatus 400 is provided to theinformation processing apparatus 1 according to an instruction from theinformation processing apparatus 1 or the like. Note that the functionof the learning apparatus 400 may be implemented by the informationprocessing apparatus 1.

In the above-described system 100, the information processing apparatus1 estimates the limit value D4 for creating the color conversion profileDP by using the machine-learned model PJ. Therefore, when determiningthe limit value D4 used to create the color conversion profile DP, it isnot necessary to repeatedly perform printing and imaging for each effectto be avoided. As a result, it is possible to reduce man-hours requiredfor creating the color conversion profile DP compared to the relatedart. Further, the machine-learned model PJ can perform machine learningbased on the imaging information DI in consideration of various effectsappeared in an image. Therefore, it is possible to easily obtain thecolor conversion profile DP with excellent color reproducibilitycompared to the related art. Hereinafter, the information processingapparatus 1 and the learning apparatus 400 will be described in detail.

2. Information Processing Apparatus 1

2-1. Configuration of Information Processing Apparatus 1

The information processing apparatus 1 includes a processing device 10,a storage device 20, a display device 30, an input device 40, and acommunication device 50. The processing device 10, the storage device20, the display device 30, the input device 40, and the communicationdevice 50 are communicably connected to one another.

The processing device 10 is a device having a function of controllingeach component of the information processing apparatus 1, the printingapparatus 200, and the imaging apparatus 300 and a function ofprocessing various data. For example, the processing device 10 includesa processor such as a central processing unit (CPU). Note that theprocessing device 10 may include a single processor or a plurality ofprocessors. Further, some or all of the functions of the processingdevice 10 may be implemented by hardware such as a digital signalprocessor (DSP), an application-specific integrated circuit (ASIC), aprogrammable logic device (PLD), or a field-programmable gate array(FPGA).

The storage device 20 is a device for storing various programsexecutable by the processing device 10 and various data to be processedby the processing device 10. The storage device 20 includes, forexample, a hard disk drive or a semiconductor memory. Note that part orall of the storage device 20 may be provided in a storage deviceexternal to the information processing apparatus 1, a server, or thelike.

The storage device 20 of the present embodiment stores the colorconversion profile creation program P1, the machine-learned model PJ,the color conversion profile DP, the test image information DG, and theimaging information DI. Here, the storage device 20 is an example of a“storage section”. Note that some or all of the color conversion profilecreation program P1, the machine-learned model PJ, the color conversionprofile DP, the test image information DG, and the imaging informationDI may be stored in a storage device external to the informationprocessing apparatus 1, a server, or the like.

The display device 30 displays various images according to control bythe processing device 10. Here, for example, the display device 30includes various display panels such as a liquid display panel and anorganic electroluminescence (EL) display panel. Note that the displaydevice 30 may be provided external to the information processingapparatus 1.

The input device 40 is a device for receiving a user operation. Forexample, the input device 40 includes a touch pad, a touch panel, or apointing device such as a mouse. Here, when the input device 40 includesthe touch panel, the input device 40 may double as the display device30. Note that the input device 40 may be provided external to theinformation processing apparatus 1.

The communication device 50 is a device for performing communicationwith the printing apparatus 200, the imaging apparatus 300, and thelearning apparatus 400 in a wired or wireless manner. For example, thecommunication device 50 includes an interface such as a Universal SerialBus (USB) interface or a local area network (LAN) interface.

In the information processing apparatus 1 having the above-describedconfiguration, the processing device 10 loads the color conversionprofile creation program P1 from the storage device 20 and executes thecolor conversion profile creation program P1. By such execution, theprocessing device 10 functions as a receiving section 11, an obtainingsection 12, an estimating section 13, a creating section 14, and anadditional learning section 15.

The receiving section 11 implements a receiving function of receiving aninput of the selection information D0. The obtaining section 12implements an obtaining function of obtaining the imaging information DIfrom the imaging apparatus 300. The estimating section 13 implements anestimating function of estimating the limit value D4 based on theselection information D0 and the imaging information DI by using themachine-learned model PJ. The creating section 14 implements a creatingfunction of creating the color conversion profile DP by using the limitvalue D4. The additional learning section 15 receives an addition of alabel LB related to suitability of the limit value D4 through a useroperation and implements an additional learning function of performingadditional machine learning of the machine-learned model PJ by using thelabel LB. For example, the additional learning section 15 performs, asthe additional machine learning, machine learning similar to that of alearning processing section 412 of the learning apparatus 400 to bedescribed later. For example, the label LB adds a weight of the limitvalue D4 to the additional machine learning. It is sufficient that theadditional learning section 15 be provided as necessary, and theadditional learning section 15 may be omitted.

2-2. Color Conversion Table TBL

FIG. 2 is a diagram illustrating an example of the color conversiontable TBL included in the color conversion profile DP. The colorconversion table TBL illustrated in FIG. 2 includes information TBL1,TBL2, and TBL3.

The information TBL1 and TBL2 are each information regarding mappingbetween coordinate values of a color space CS1 and coordinate values ofa color space CS2. More specifically, the information TBL1 is an A2Btable for converting the coordinate values (C_(i), M_(i), Y_(i), andK_(i)) of the color space CS1 into the coordinate values (L_(i), a_(i),and b_(i)) of the color space CS2. A variable i is a variable foridentifying a grid point GD1 set in the color space CS1. The grid pointsGD1 are arranged at equal intervals in a direction along each axis ofthe color space CS2. Meanwhile, the information TBL2 is a B2A table forconverting coordinate values (L_(j), a_(j), and b_(j)) of the colorspace CS2 into the coordinate values (C_(j), M_(j), Y_(j), and K_(j)) ofthe color space CS1. A variable j is a variable for identifying a gridpoint GD2 set in the color space CS2. The grid points GD2 are arrangedat equal intervals in a direction along each axis of the color space.

The color space CS1 is, for example, a device-dependent color space.FIG. 2 illustrates a case in which the color space CS1 is a CMYK colorspace. Meanwhile, the color space CS2 is a profile connection space(PCS) and is, for example, a device-independent color space. FIG. 2illustrates a case in which the color space CS2 is the CIELAB colorspace. Regarding the color space type, the color space CS1 is simply acolor space that an output device can use. The color space CS1 is notlimited to a CMYK color space and may be, for example, a CMY color spaceor a color space specific to an output device. The color space CS2 issimply a device-independent color space. The color space CS2 is notlimited to the CIELAB color space and may be, for example, an XYZ colorspace.

The information TBL3 is information regarding mapping between thecoordinate values (C_(j), M_(j), Y_(j), and K_(j)) of the color spaceCS2 and values (c_(j), m_(j), y_(j), and k_(j)) each indicating anamount of ink. For example, the information TBL3 is a lookup table forconverting the coordinate values after the conversion in the informationTBL2, which is the B2A table described above, into amounts of ink. Thevalues (c_(j), m_(j), y_(j), and k_(j)) each correspond to, for example,an ink color of the ink ejecting head 210 described above, and each is agradation value indicating an amount of ink of a corresponding color tobe used.

2-3. Color Conversion Profile (DP) Creation Method

FIG. 3 is a diagram illustrating a flow of creating the color conversionprofile DP. As illustrated in FIG. 3, the color conversion profile (DP)creation method includes: receiving input of the selection informationD0 (Step S101); obtaining the imaging information DI (Step S102);estimating the limit value D4 (Step S103); and creating the colorconversion profile DP (Step S104). Hereinafter, the respective stepswill be sequentially described. Note that the order of Steps S101 andS102 is not limited to that illustrated in FIG. 3. Step S101 may beperformed after Step S102, or Step S101 and Step S102 may be performedat the same time.

In Step S101, input of the selection information D0 is received. Inputis performed by using, for example, the input device 40. Here, forexample, an image for selecting or inputting the selection informationD0 is displayed on the display device 30.

In Step S102, the imaging information DI is obtained. The imaginginformation DI is obtained by capturing an image printed on a printingmedium by the printing apparatus 200. Hereinafter, the image will bedescribed.

FIG. 4 is a diagram illustrating an image G which is an example of animage used in creating the color conversion profile DP. The image G isformed on a printing surface MP0 of a printing medium MP whileappropriately varying a combination of ink of four colors used by theprinting apparatus 200 described above and varying a gradation value ofthe ink. The image G illustrated in FIG. 4 is constituted by a pluralityof unit images GP. The plurality of unit images GP are grouped intoimage groups G1, G2, and G4. Note that arrangement of the plurality ofunit images GP is not limited to the arrangement illustrated in FIG. 4.

The image group G1 is constituted by a plurality of monochrome unitimages GP formed using the ink of four colors. The image group G1illustrated in FIG. 4 is constituted by n unit images GP of whichgradation values of each color of the ink of four colors are differentfrom each other. The image group G2 is constituted by a plurality ofsecondary-color unit images GP formed using the ink of four colors. Theimage group G2 illustrated in FIG. 4 is constituted by n unit images GPof which gradation values of each secondary color of the ink of fourcolors are different from each other. The image group G4 is constitutedby a plurality of quaternary-color unit images GP formed using the inkof four colors. The image group G4 illustrated in FIG. 4 is constitutedby n unit images GP of which gradation values of the ink are differentfrom each other. Here, n is a natural number of 2 or more, andpreferably n is 10 or more and 100 or less. Note that the range of thegradation value of the ink for forming the plurality of unit images GPis appropriately determined.

FIG. 5 is a diagram illustrating an example of a unit image GPconstituting the image G illustrated in FIG. 4. The unit image GPillustrated in FIG. 5 has a quadrilateral shape. In the exampleillustrated in FIG. 5, a line pattern PL1 extending from one end of oneside in a diagonal direction with respect to each side, and a linepattern PL2 extending from the other end of the one side in a diagonaldirection with respect to each side are drawn in the unit image GP. Theline pattern PL1 divides the unit image GP into a region PA1 and aregion PA2. The line pattern PL2 divides the region PA2 into a regionPA2 a and a region PA2 b. The regions PA1 and PA2 are each printed withink of a color corresponding to the above-described image group. Theline patterns PL1 and PL2 are each printed with ink of a color differentfrom that of the regions PA1 and PA2. Note that the shape of each unitimage GP is not limited to the shape illustrated in FIG. 5 or the like.

It is possible to identify a relationship between the amount of inkprinted on the printing surface MP0 per unit area and the occurrence ofbleeding, aggregation, and overflowing of the ink by observing theprinting surface MP0 on which the image G is printed. Hereinafter, thebleeding, aggregation, and overflowing of the ink will be brieflydescribed.

FIG. 6 is a diagram illustrating an example of a state where overflowingof the ink occurs. Here “overflowing of the ink” refers to a state wherethe ink overflows out of the original region of the unit image GP due tothe supplied amount of ink being excessive relative to the ink absorbingcapacity of the printing medium MP, and thus the shape of the unit imageGP is lost. FIG. 6 illustrates an example in which the shape of aboundary between the regions PA1 and PA2, and the line patterns PL1 andPL2 is lost.

FIG. 7 is a diagram illustrating an example of a state where bleeding ofthe ink occurs. Here “bleeding of the ink” refers to a state where theink overflows out of an original region of the unit image GP due to thesupplied amount of ink being excessive relative to the ink holdingcapacity of the printing medium MP, and thus the distinctiveness ofoutlines of the unit image GP is decreased. FIG. 7 illustrates anexample in which the distinctiveness of a boundary between the regionsPA1 and PA2, and the line patterns PL1 and PL2 is decreased.

FIG. 8 is a diagram illustrating an example of a state where aggregationof the ink occurs. Here “aggregation of the ink” refers to a state whereink density unevenness is evident in the unit image GP due toagglomerated dispersoids when using a dispersion-type ink. FIG. 8illustrates an example in which density unevenness is evident in each ofthe regions PA1 and PA2.

Although not illustrated, a tone jump in gradation or the like isevident as a color difference or density difference between unit imagesGP in the printed image G.

In Step S103, the estimating section 13 estimates, based on theselection information D0 and the imaging information DI, by using themachine-learned model PJ the limit value D4 indicating a maximum valueof the amount of ink to be used for printing on the printing medium MPby the printing apparatus 200 per unit area. The limit value D4 isrepresented as information (C_(i), M_(i), Y_(i), and K_(i)) eachindicating the amount of ink of a corresponding color.

FIG. 9 is a diagram for describing the estimating section 13 forestimating the limit value D4. The machine-learned model PJ is amachine-learned model that outputs the limit value D4 in response to theinput of the imaging information DI and the selection information D0.Specifically, the machine-learned model PJ is implemented by combining aplurality of coefficients with a program causing the processing device10 to perform a calculation for generating the limit value D4 based onthe imaging information DI and the selection information D0, theplurality of coefficients being applied to the calculation. For example,the program is a program module constituting artificial intelligencesoftware. The plurality of coefficients are set by, for example, deeplearning using the data set DS in the learning apparatus 400 to bedescribed later. As a suitable example, FIG. 9 illustrates a case inwhich the machine-learned model PJ is a mathematical model such as adeep neural network including an input layer, an output layer, and anintermediate layer.

In Step S104, the creating section 14 creates the color conversionprofile DP by using the limit value D4. Specifically, the creatingsection 14 creates the color conversion profile DP so that the amount ofink of each single color, secondary color, or quaternary color does notexceed an estimation result obtained by the estimating section 13.

In the above-described information processing apparatus 1, theestimating section 13 estimates the limit value D4 for creating thecolor conversion profile DP by using the machine-learned model PJ.Therefore, when determining the limit value D4 to be used to create thecolor conversion profile DP, it is not necessary to repeatedly performprinting and imaging for each effect to be avoided. As a result, it ispossible to reduce man-hours required for creating the color conversionprofile DP compared to the related art. Further, the machine-learnedmodel PJ can perform machine learning in consideration of variouseffects evident in an image based on the imaging information DI.Therefore, it is possible to easily obtain the color conversion profileDP with excellent color reproducibility compared to the related art.

In the present embodiment, the machine-learned model PJ further learns,by machine learning, a relationship with the type of the color spaceused for the color conversion profile DP. Therefore, when the selectioninformation D0 includes the color space information D2 regarding thetype of the color space, it is possible to obtain the color conversionprofile DP with a reduced color difference before and after the colorconversion.

Here, a value of a color difference ΔE based on the mapping in the colorconversion profile DP in the color space is preferably 3.0 or less, atwhich the color difference is hardly noticed by a person in a separatelypositioned comparison. In this case, it is possible to obtain a colorconversion profile DP with substantially no color difference before andafter the color conversion.

Further, the machine-learned model PJ further learns, by machinelearning, a relationship with the type of the coloring material.Therefore, when the selection information D0 includes the coloringmaterial-type information D3 regarding the type of the coloringmaterial, it is possible to obtain the color conversion profile DP byconsidering the type of the coloring material.

In addition, the additional learning section 15 receives an addition ofthe label LB related to suitability of the limit value D4 through a useroperation and performs additional machine learning of themachine-learned model PJ by using the label LB. Therefore, it ispossible to improve color reproducibility of the color conversionprofile DP afterward.

3. Learning Apparatus 400

As illustrated in FIG. 1, the learning apparatus 400 includes aprocessing device 410 and a storage device 420, and the processingdevice 410 and the storage device 420 are communicably connected to eachother. Although not illustrated, the learning apparatus 400 includes acommunication device that can perform communication with the informationprocessing apparatus 1. The communication device is configured similarlyto the communication device 50 of the information processing apparatus 1described above. Note that the learning apparatus 400 may include adevice similar to the display device 30 and the input device 40 of theinformation processing apparatus 1.

The processing device 410 is a device having a function of controllingeach component of the learning apparatus 400 and a function ofprocessing various data. For example, the processing device 410 includesa processor such as a CPU. Note that the processing device 410 mayinclude a single processor or a plurality of processors. Further, someor all of the functions of the processing device 410 may be implementedby hardware such as a DSP, an ASIC, a PLD, and a FPGA.

The storage device 420 is a device for storing various programs executedby the processing device 410 and various data processed by theprocessing device 410. The storage device 420 includes, for example, ahard disk drive or a semiconductor memory. Note that part or all of thestorage device 420 may be provided in a storage device external to thelearning apparatus 400, a server, or the like.

The storage device 420 of the present embodiment stores a learningprogram P2, the data set DS, and the machine-learned model PJ. Note thatsome or all of the learning program P2, the data set DS, and themachine-learned model PJ may be stored in a storage device external tothe learning apparatus 400, a server, or the like.

In the learning apparatus 400 having the above-described configuration,the processing device 410 loads the learning program P2 from the storagedevice 420 and executes the learning program P2. During execution, theprocessing device 410 functions as an input section 411 and the learningprocessing section 412.

The input section 411 implements a function of receiving input of thedata set DS in which the selection information D0, the imaginginformation DI, and the limit value D4 a are mapped to one another. Thelearning processing section 412 implements a function of generating themachine-learned model PJ based on the data set DS.

FIG. 10 is a diagram for describing machine learning for generating themachine-learned model PJ. A plurality of data sets DS are used for themachine learning of the machine-learned model PJ. Each data set DSincludes the imaging information DI and includes the selectioninformation D0 and the limit value D4 a corresponding to the imaginginformation DI. The selection information D0 and the imaging informationDI in the data set DS may be generated by using an apparatus separatefrom the information processing apparatus 1. The limit value D4 a in thedata set DS is a label corresponding to a correct value for the imaginginformation DI and the selection information D0 in the data set DS. Thelabel is determined by a manager or the like based on a state of theimage G described above. Here, an upper limit value when one or more ofthe effects to be avoided such as overflowing, bleeding, and aggregationof the ink were avoided in the past under the same condition as theselection information D0 and the imaging information DI can be used asthe limit value D4 a.

The learning processing section 412 sets a plurality of coefficients ofthe machine-learned model PJ through supervised machine learning using aplurality of data sets DS. Specifically, the learning processing section412 updates the plurality of coefficients of the machine-learned modelPJ so that a difference between the limit value D4 a included in thedata set DS and a limit value D4 b output by the provisionalmachine-learned PJ in response to input of the selection information D0and the imaging information DI in the data set DS is decreased. Forexample, the learning processing section 412 repeatedly updates theplurality of coefficients of the machine-learned model PJ by aback-propagation method so that an evaluation function indicating thedifference between the limit value D4 a and the limit value D4 b issignificantly reduced. The plurality of coefficients of themachine-learned model PJ set through the machine learning describedabove are stored in the storage device 420. After the machine learningdescribed above is performed, the machine-learned model PJ outputs astatistically appropriate limit value D4 with respect to unknownselection information D0 and imaging information DI based on a potentialtendency between the selection information D0 and the imaginginformation DI, and the limit value D4 a in the plurality of data setsDS.

In the above-described learning apparatus 400, it is possible to obtainthe machine-learned model PJ that performs machine learning inconsideration of various effects evident in the image G used in themachine learning. For example, in the present embodiment, it is possibleto obtain the machine-learned model PJ by considering not only theoverflowing, bleeding, aggregation, and color saturation of the ink, butalso the tone jump in gradation, the color gamut, or the like.

4. Modified Example

Hereinabove, the information processing apparatus, the color conversionprofile creation method, the color confirmation profile creationprogram, and the learning apparatus according to the present disclosurehave been described based on the illustrated embodiments, but thepresent disclosure is not limited thereto. Further, a configuration ofeach section according to the present disclosure can be substituted withan appropriate configuration that can implement the same functions asthe above-described embodiments, and any appropriate configuration canalso be added.

FIG. 11 is a schematic diagram illustrating an example of aconfiguration of a system 100A using an information processing apparatus1A according to a modified example. In the above-described embodiment, acase in which the information processing apparatus 1 executes the colorconversion profile creation program P1 has been described by way of anexample. Here, as illustrated in FIG. 11, the information processingapparatus 1A functioning as a server may execute the color conversionprofile creation program P1 and some or all of functions implemented bythe execution may be provided to a client 500.

In FIG. 11, the information processing apparatus 1A is communicablyconnected to each of the client 500, printing apparatuses 200-1, 200-2,and 200-3 via a communication network NW including the Internet. Theinformation processing apparatus 1A is a computer that can execute thecolor conversion profile creation program P1. The printing apparatuses200-1 to 200-3 are each configured similarly to the printing apparatus200 described above. Note that the information processing apparatus 1Amay double as the learning apparatus 400 described above.

Further, in the above-described embodiment, a case in which the limitvalue D4 represents a maximum value of the amount of ink to be used forprinting on the printing medium by the printing apparatus 200 per unitarea has been described by way of an example, but the present disclosureis not limited thereto. For example, the estimating section 13 canestimate, as the limit value D4, a minimum value of the amount of ink tobe used for printing on the printing medium by the printing apparatus200 per unit area. In this case, the minimum value of the amount of inkto be used for printing on the printing medium by the printing apparatus200 per unit area may be used as the limit value D4 a in the data setDS.

In addition, in the above-described embodiment, a case in which ink isused as the coloring material has been described by way of an example,but the present disclosure is not limited thereto, and for example,toner may be used as the coloring material. That is, the printingsection may be, for example, an electrophotographic printer. In thiscase, examples of the effect to be avoided can include peeling of thetoner.

Further, the printing medium may be a printing medium for sublimationtransfer. In this case, the imaging information used for creation of thecolor conversion profile or generation of the machine-learned model maybe imaging information obtained by capturing an image of the printingmedium, or may be imaging information obtained by capturing an image ofa medium subjected to image transfer from the printing medium.

In addition, in the above-described embodiment, a case in which theadditional machine learning of the machine-learned model PJ is performedin the information processing apparatus 1 has been described by way ofexample, but the present disclosure is not limited thereto, and theadditional machine learning may be performed in the learning apparatus400. In this case, the machine-learned model PJ that performed theadditional machine learning is provided to the information processingapparatus 1 in a timely manner.

What is claimed is:
 1. An information processing apparatus comprising: astorage section configured to store a machine-learned model thatlearned, by machine learning, a relationship between a type of aprinting medium, an amount of a coloring material on the printing mediumper unit area, and an image printed on the printing medium; a receivingsection configured to receive input of selection information includingmedium-type information regarding a type of the printing medium; anobtaining section configured to obtain imaging information obtained bycapturing the image printed on the printing medium by a printing sectionthat performs printing by using the coloring material; an estimatingsection configured to estimate, based on the selection information andthe imaging information, by using the machine-learned model a limitvalue indicating a maximum value or a minimum value of an amount of thecoloring material to be used for printing on the printing medium by theprinting section per unit area; and a creating section configured tocreate, by using the limit value, a color conversion profile includinginformation regarding mapping between a coordinate value in a colorspace and an amount of the coloring material.
 2. The informationprocessing apparatus according to claim 1, wherein the machine-learnedmodel further learns, by machine learning, a relationship with a type ofthe color space, and the selection information includes color spaceinformation regarding the type of the color space.
 3. The informationprocessing apparatus according to claim 2, wherein a value of a colordifference ΔE based on the mapping in the color space is 3.0 or less. 4.The information processing apparatus according to claim 1, wherein themachine-learned model further learns, by machine learning, arelationship with a type of the coloring material, and the selectioninformation includes coloring material-type information regarding thetype of the coloring material.
 5. The information processing apparatusaccording to claim 1, further comprising an additional learning sectionconfigured to receive, through a user operation, an addition of a labelrelated to suitability of the limit value and perform additional machinelearning of the machine-learned model by using the label.
 6. A colorconversion profile creation method comprising: preparing amachine-learned model that learned, by machine learning, a relationshipbetween a type of a printing medium, an amount of a coloring material onthe printing medium per unit area, and an image printed on the printingmedium; receiving input of selection information including medium-typeinformation regarding a type of the printing medium and obtainingimaging information obtained by capturing the image printed on theprinting medium by a printing section that performs printing by usingthe coloring material; estimating, based on the selection informationand the imaging information, by using the machine-learned model a limitvalue indicating a maximum value or a minimum value of an amount of thecoloring material to be used for printing on the printing medium by theprinting section per unit area; and creating, by using the limit value,a color conversion profile including information regarding mappingbetween a coordinate value in a color space and an amount of thecoloring material.